Asus ESC1000G2 manual Part List

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Power Supply

1350W 80+ Gold Single Power Supply (following different configuration by region)

Environment

Operation temperature: 10~ 35/ Non operation temperature: -40~ 70

Non operation humidity: 20% ~ 90% ( Non condensing)

Accessory Box

1 x ESC1000 G2 User's Guide

1x Bag of Screws

1x AC Power Cable

1x P9X79 WS Support DVD

1x COM prot Cable

Optional Items

DVD-RW (17G141147005) (DVD-RW default for North America) ASUS ESC1000 G2 Ball Bearing Rail Kit (13GS0K00M040 + 13GS170AM040-1)

Cooler for CPU 150W upgrade kit (13G070795020) Front Fan (80mm x 25mm) (13G074186020)

Note

*DDR3 1866 can only be supported with Intel® Core™ i7 Processors for LGA 2011 Socket and Intel® Xeon® processor E5-1600 v2 product family, refer to www.asus.com for latest momory AVL update

Part List

MB

ASUS P9X79 WS Workstation board

HDD Backplane

1 x SAS/SATA Backplane (BP4LX-TRS8) (60SC00M0-SC0B02)

Front Panel Board

1 x Front I/O Board (FPB-TRS8A) (61-C1SDJ1-01)

CPU Heatsink/ Cooler

1 x 150W CPU Cooler (13G070795020)

Power Supply Unit

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Contents Quad-GPU support unleashes more computing power Dual Intel Ethernet for fast networking Maximum power supply with DIGI+ for demanding workloadsAsus SSD Caching USB 3.0 BoostSpecifications Weight DimensionsNetworking Multi-GPU SupportPart List HDD Cage HDD TrayFan Fan Holder

ESC1000G2 specifications

The Asus ESC1000G2 is a high-performance workstation designed for professional users in fields such as AI, machine learning, data science, and graphics-intensive applications. This system emphasizes powerful processing capabilities, exceptional graphics performance, and a modular design that provides users with flexibility and scalability.

At the heart of the ESC1000G2 is its support for the latest Intel Xeon or Core processors, allowing users to choose a configuration that best meets their performance needs. This ensures robust computing power to handle demanding workloads, regardless of the user's area of expertise. With multiple CPU options available, users can tailor their setup for tasks ranging from complex simulations to 3D rendering.

One of the standout features of the ESC1000G2 is its capacity for dual NVIDIA graphics cards, supporting the latest Quadro or GeForce GPUs. This dual GPU configuration makes it ideal for users needing superior graphical output, as it allows for advanced rendering and real-time visualization. The design also supports powerful GPU-accelerated compute tasks, resulting in faster completion times for data-heavy operations.

With up to 128GB of DDR4 memory, the ESC1000G2 provides ample RAM for multitasking and high-performance computing. This ensures that users can work with large datasets, run multiple applications simultaneously, and perform intricate calculations without experiencing latency or performance dips.

Storage options in the ESC1000G2 are versatile, featuring multiple drive bays that accommodate both SSDs and traditional HDDs. Users can implement a combination of high-speed NVMe SSDs for critical applications and larger HDDs for data storage, allowing for a balance between performance and capacity. This flexibility is essential for workflows that require swift data access alongside abundant storage.

The ESC1000G2 also prioritizes connectivity and expandability. Equipped with multiple USB ports, Thunderbolt 3 compatibility, and high-speed Ethernet ports, it allows users to connect various peripherals and network devices seamlessly. The system’s design includes ample PCIe slots, making it easier for users to upgrade their hardware as technology advances.

In summary, the Asus ESC1000G2 workstation stands out for its powerful processing capabilities, superior graphics performance, and flexible storage options. With its modular design and extensive connectivity features, it caters to the needs of professionals looking for reliability and maximum efficiency in demanding environments. Whether in artificial intelligence, video editing, or large-scale data analysis, the ESC1000G2 proves to be a valuable asset for today's performance-driven workloads.